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The most common mistake people make in mid-career transition to AI

 AI will impact many jobs  and COVID has only accelerated this trend   
 
Based on my teaching at the University of Oxford for AI, we see a big demand for people wanting to transition to AI due to the current environment. 
If you are at the early stage of your career (less than two years out of Uni), a transition to AI is relatively easier
But you may need a different strategy for a mid career transition to AI
Why?
In the early stage of your career, all things being equal,  you would add more value than the company pays you
But about 4 plus years into your career, that changes 
At that time, you are moving away from core development (directly creating value) and also your are being paid more  - leading to a decline in value in pure economic terms 
(I know this is a generalization - it does not apply to everyone but it is valid overall)
You then end up with a variety of titles that give incremental pay basically do the same thing
At this point, many a career could languish
Hence, it is at this point that people explore alternate careers paths especially to AI 
But mid-career transition to AI is a bit different
The biggest mistake by far which people make - is ignoring what they already know 
The general thinking goes like this
You focus on learning Python and equate knowledge of coding with AI
Coding is indeed necessary but is not a sufficient in itself
Because ultimately to add value, you would need to more than coding alone
So, now let's consider a different starting point
You learn Python but you also consider what you know
To give you an example, suppose you are an engineer
 
An engineer could model a problem using specific techniques such as mathematical modelling (see my previous blog on this topic HERE)
Once you model a problem, you could work on model evaluation criteria for specific problems and compare them to industry benchmarks
 
In other words, you could co-relate problems in your industry to data science 
 
The good thing is, data science will be in every field
Hence, the trick to transition to data science is to find and solve big problems in your industry based on ideas you already know from your experience in the industry
Image source: General Assembly - United Nations

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Comment by Jörg Narr on November 26, 2020 at 1:03am

Thank you, Ajit. Fully agree. Times will change. Senior roles can bridge the gap between Data Scientists and Business/Use Cases.

Comment by ajit jaokar on November 25, 2020 at 11:24pm

thanks for your comments @Jorg 

I wanted to stress two points in this post

a)  Midcareer transition is not the same as early stage due to the need to add more value

b)  The previous blog was an idea for illustration 

c)  re " I do see many Data Scientists working on problems and optimizing solutions in companies which remind me more on "investigating" than solving issues." this is indeed true. However, I see that times will change. ie people will go from investigation to solving more complex problems
thanks for your comments! rgds ajit

Comment by Jörg Narr on November 25, 2020 at 10:30pm

I generally like the post but I think particularly the conclusion is quite brief and seems to me to be mainly used to create more traffic on previous posts. Nevertheless I second your thoughts. In my experience sufficient AI knowledge, frameworks, and technology are here whilst clever ideas to apply AI to real-world problems is quite rare. I do see many Data Scientists working on problems and optimizing solutions in companies which remind me more on "investigating" than solving issues.

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